Edge Trends: The Evolution of Identity

The Evolution of Identity

Each year millions of consumers fall victim to identity theft, costing the ecosystem billions of dollars. Large-scale data breaches are often the root cause: over the past several years, thieves have stolen the personal information of hundreds of millions of Americans. The difficulty of restoring or “reclaiming” one’s identity is a particularly insidious aspect of this crime. By design, identity elements like a person’s name or Social Security number are difficult to change, and since most institutions usually rely on a simple combination of name, date of birth, and Social Security number to identify a person, it is increasingly difficult for them to defend against fraud losses.

In this talk, we’ll discuss some approaches used in the industry to defend enterprises and the consumers they serve. We’ll also talk about some larger industry trends, including the increasing role that digital security plays in defending against identity theft. Finally, we’ll offer some conjectures about how identities will be managed in the future and some technologies that could lead to a radical change in how people prove who they are.

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About the Speakers

Daniel Grady is a data scientist at ID Analytics, where he builds systems for identifying anomalies in consumer transactions and on understanding how data breaches affect identity exposure and risk. He’s particularly interested in network analysis and in finding ways to apply those ideas in different problem domains; in the past, he’s worked on pattern matching and anomaly detection in relational data streams, and on modeling the process by which diseases spread across transportation networks. He received in Ph.D. in applied mathematics from Northwestern University in 2012.

Reza Farsian is currently a data scientist at ID Analytics with more than 10 years of experience in industry and academia building analytical models. For the past 5 years, he has focused on solving problems in financial fraud and risk space by developing data driven models. One of his main motivations is to connect different disciplines and use methods across various problems. Reza holds a PhD from UC San Diego in Physics with a specialization in Computational Neuroscience.